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arXiv · 2610.19561

REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning

Yi Ding·Mengyu Wang·Min Zhou·Menglin Yang
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Abstract

An early mistake in the generated reasoning can constrain all subsequent reasoning. Repeated-generation methods, like MCMC-based approaches, attempt to improve output quality by sampling multiple generations, but their effectiveness can be limited by randomly selecting where to regenerate. On MATH500 with Qwen2.5-Math-7B, we find that incorrect responses have substantially higher mean token entropy than correct responses, with an average within-question relative increase of approximately 112%, suggesting that entropy can signal reasoning errors. Motivated by this finding, we introduce REWIND, a training-free controller that uses entropy dynamics to propose targeted suffix regenerations. Across three model families and five benchmarks, REWIND outperforms all baselines, with gains of 0.51-10.00 percentage points over the strongest baseline. These results demonstrate the effectiveness of targeted, uncertainty-guided trajectory repair across models and tasks. Our code is available at https://anonymous.4open.science/r/rewind-D310.

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Yi Ding, Mengyu Wang, Min Zhou, Menglin Yang. 2026-10-14. REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning. https://arxiv.org/abs/2610.19561

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